Extending HEVC with a Texture Synthesis Framework using Detail-aware Image Decomposition

Bastian Wandt, Thorsten Laude, B. Rosenhahn, J. Ostermann
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引用次数: 3

Abstract

In recent years, there has been a tremendous improvement in video coding algorithms. This improvement resulted in 2013 in the standardization of the first version of High Efficiency Video Coding (HEVC) which now forms the state-of-theart with superior coding efficiency. Nevertheless, the development of video coding algorithms did not stop as HEVC still has its limitations. Especially for complex textures HEVC reveals one of its limitations. As these textures are hard to predict, very high bit rates are required to achieve a high quality. Texture synthesis was proposed as solution for this limitation in previous works. However, previous texture synthesis frameworks only prevailed if the decomposition into synthesizable and non-synthesizable regions was either known or very easy. In this paper, we address this scenario with a texture synthesis framework based on detail-aware image decomposition techniques. Our techniques are based on a multiple-steps coarse-to-fine approach in which an initial decomposition is refined with awareness for small details. The efficiency of our approach is evaluated objectively and subjectively: BD-rate gains of up to 28.81% over HEVC and up to 12.75% over the closest related work were achieved. Our subjective tests indicate an improved visual quality in addition to the bit rate savings.
使用细节感知图像分解的纹理合成框架扩展HEVC
近年来,视频编码算法有了很大的改进。这一改进导致了2013年第一版高效视频编码(HEVC)的标准化,现在形成了具有卓越编码效率的最新状态。然而,视频编码算法的发展并没有停止,HEVC仍然有它的局限性。特别是对于复杂的纹理,HEVC揭示了它的局限性之一。由于这些纹理很难预测,因此需要非常高的比特率来实现高质量。纹理合成在以前的工作中被提出来解决这一限制。然而,以前的纹理合成框架只有在已知或非常容易分解为可合成和不可合成区域的情况下才会流行。在本文中,我们使用基于细节感知图像分解技术的纹理合成框架来解决这种情况。我们的技术是基于一个多步骤的从粗到精的方法,在这个方法中,一个初始的分解被细化到小细节。我们的方法的效率得到了客观和主观的评价:与HEVC相比,bd率提高了28.81%,与最接近的相关工作相比,bd率提高了12.75%。我们的主观测试表明,除了比特率节省之外,视觉质量也得到了改善。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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